Mortality in the SuperMIX cohort of people who inject drugs in Melbourne, Australia: a prospective observational study
Bibliographic record
Abstract
AIMS: To measure mortality rates and factors associated with mortality risk among participants in the SuperMIX study, a prospective cohort study of people who inject drugs. DESIGN: A prospective observational study using self-reported behavioural and linked mortality data. SETTING: Melbourne, Australia. PARTICIPANTS/CASES: A total of 1209 people who inject drugs (67% male) followed-up between 2008 and 2019 for 6913 person-years (PY). MEASUREMENTS: We linked participant identifiers from SuperMIX to the Australian National Death Index and estimated all-cause and drug-related mortality rates and standardized mortality ratios (SMRs). We used Cox regression to examine associations between mortality and fixed and time-varying socio-demographic, alcohol and other drug use and health service-related exposures. FINDINGS: Between 2008 and 2019 there were 76 deaths in the SuperMIX cohort. Of those with a known cause of death (n = 68), 35 (51%) were drug-related, yielding an all-cause mortality rate of 1.1 per 100 PY [95% confidence interval (CI) = 0.88-1.37] with an estimated SMR of 16.64 (95% CI = 13.29-20.83) and overall accidental drug-induced mortality rate of 0.5 per 100 PY (95% CI = 0.36-0.71). Reports of recent use of ambulance services [adjusted hazard ratio (aHR) = 3.77, 95% CI =1.78-7.97] and four or more incarcerations (aHR = 2.78, 95% CI = 1.55-4.99) were associated with increased mortality risk. CONCLUSIONS: In Melbourne, Australia, mortality among people who inject drugs appears to be positively associated with recent ambulance attendance and experience of incarceration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".